This paper presents a new Bayesian, infinite mixture model based, clustering approach, specifically designed for time-course microarray data. The problem is to group together genes which have “similar” expression profiles, given the set of noisy measurements of their expression levels over a...

The objective of the present paper is to develop a minimax theory for the varying coefficient model in a non-asymptotic setting. We consider a high- dimensional sparse varying coefficient model where only few of the covariates are present and only some of those covariates are time dependent. Our...

The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression...

A truly functional Bayesian method for detecting temporally differentially expressed genes between two experimental conditions is presented. The method distinguishes between two biologically different set ups, one in which the two samples are interchangeable, and one in which the second sample...

Under the assumption that both convolution densities, g and q, have finite degrees of smoothness, we construct a nonlinear wavelet estimator of the unknown density g based on wavelets with bounded supports. We show that this estimator provides local adaptivity to the unknown smoothness of g and,...

We consider independent pairs (X1, [Sigma]1), (X2, [Sigma]2), ..., (Xn, [Sigma]n), where each[Sigma]iis distributed according to some unknown density functiong([Sigma]) and, given[Sigma]i=[Sigma],Xihas conditional density functionq(x|[Sigma]) of the Wishart type. In each pair the...